Generative AI Insights, an InfoWorld blog open to outside contributors, provides a venue for technology leaders to explore and discuss the challenges and opportunities presented by generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content.
Why relying on retrieval-augmented generation and prompt engineering is preferable to investing in model training and fine-tuning.
While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.
Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.
AI red teaming offers an innovative, proactive method for strengthening AI while mitigating potential risks, helping organizations avoid costly AI incidents. Here’s how it works.
Developers are tired of hearing about AI as a panacea. The backlash may be just what organizations need to effectively implement the technology.
Small language models shine for domain-specific or specialized use cases, while making it easier for enterprises to balance performance, cost, and security concerns.
1.7 million AI chats in 30 days... and seven big lessons Campfire learned about building better AI chat products.
What’s the best way to store, search, and analyze content not based on their technical characteristics but on their meaning?
How Gencore AI enables the construction of production-ready generative AI pipelines using any data system, vector database, AI model, and prompt endpoint.
How can enterprises secure and manage the expanding ecosystem of AI applications that touch sensitive business data? Start with a governance framework.
Failed AI projects waste time and resources, damage reputations, and stifle innovation. To succeed with AI, put the necessary practices in place to ensure high-quality data.
The potential for generative AI to deliver a significant return on investment is being demonstrated by early adopters across various industries. My company provides one example.
By giving developers the freedom to explore AI, organizations can remodel the developer role and equip their teams for the future.
Python developers are uniquely positioned to succeed in the AI era, with a little help from upskilling.
SB 1047 missed the mark. A far better solution to managing AI risks would be a unified federal regulatory approach that is adaptable, practical, and focused on real-world threats.
How high-quality, synthetically designed data sets enable the development of specialized AI models.
Five of the most common and complex challenges organizations face in putting large language models into production and how to tackle them.
Combining knowledge graphs with retrieval-augmented generation can improve the accuracy of your generative AI application, and generally can be done using your existing database.
LLMs are powering breakthroughs and efficiencies across industries. When choosing a model, enterprises should consider its intended application, speed, security, cost, language, and ease of use.
Once you get your retrieval-augmented generation system working effectively, you may face new challenges in scalability, user experience, and operational overhead.
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